<?xml version="1.0" standalone="yes"?>
<Paper uid="C04-1188">
  <Title>Information Extraction for Question Answering: Improving Recall Through Syntactic Patterns</Title>
  <Section position="3" start_page="0" end_page="0" type="relat">
    <SectionTitle>
2 Related Work
</SectionTitle>
    <Paragraph position="0"> There is a large body of work on extracting semantic information using lexical patterns. Hearst (1992) explored the use of lexical patterns for extracting hyponym relations, with patterns such as &amp;quot;such as.&amp;quot; Berland and Charniak (1999) extract &amp;quot;part-of&amp;quot; relations. Mann (2002) describes a method for extracting instances from text by means of part-of-speech patterns involving proper nouns.</Paragraph>
    <Paragraph position="1"> The use of lexical patterns to identify answers in corpus-based QA received lots of attention after a team taking part in one of the earlier QA Tracks at TREC showed that the approach was competitive at that stage (Soubbotin and Soubbotin, 2002; Ravichandran and Hovy, 2002). Different aspects of pattern-based methods have been investigated since.</Paragraph>
    <Paragraph position="2"> E.g., Ravichandran et al. (2003) collect surface patterns automatically in an unsupervised fashion using a collection of trivia question and answer pairs as seeds. These patterns are then used to generate and assess answer candidates for a statistical QA system. Fleischman et al. (2003) focus on the precision of the information extracted using simple part-of-speech patterns. They describe a machine learning method for removing noise in the collected data and showed that the QA system based on this approach outperforms an earlier state-of-the-art system. Similarly, Bernardi et al. (2003) combine the extraction of surface text patterns with WordNet-based filtering of name-apposition pairs to increase precision, but found that it hurt recall more than it helped precision, resulting in fewer questions answered correctly when the extracted information is deployed for QA.</Paragraph>
    <Paragraph position="3"> The application of deeper NLP methods has also received much attention in the QA community. The open-domain QA system by LCC (Moldovan et al., 2002) uses predicate-argument relations and lexical chaining to actually prove that a text snippet provides an answer to a question. Katz and Lin (2003) use syntactic dependency parsing to extract relations between words, and use these relations rather than individual words to retrieve sentences relevant to a question. They report a substantial improvement for certain types of questions for which the usual term-based retrieval performs quite poorly, but argue that deeper text analysis methods should be applied with care.</Paragraph>
  </Section>
class="xml-element"></Paper>